Face Generation

In this project, you'll use generative adversarial networks to generate new images of faces.

Get the Data

You'll be using two datasets in this project:

  • MNIST
  • CelebA

Since the celebA dataset is complex and you're doing GANs in a project for the first time, we want you to test your neural network on MNIST before CelebA. Running the GANs on MNIST will allow you to see how well your model trains sooner.

If you're using FloydHub, set data_dir to "/input" and use the FloydHub data ID "R5KrjnANiKVhLWAkpXhNBe".

In [26]:
data_dir = './data'

# FloydHub - Use with data ID "R5KrjnANiKVhLWAkpXhNBe"
#data_dir = '/input'


"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import helper

helper.download_extract('mnist', data_dir)
helper.download_extract('celeba', data_dir)
Found mnist Data
Found celeba Data

Explore the Data

MNIST

As you're aware, the MNIST dataset contains images of handwritten digits. You can view the first number of examples by changing show_n_images.

In [27]:
show_n_images = 23

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
%matplotlib inline
import os
from glob import glob
from matplotlib import pyplot

mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'mnist/*.jpg'))[:show_n_images], 28, 28, 'L')
pyplot.imshow(helper.images_square_grid(mnist_images, 'L'), cmap='gray')
Out[27]:
<matplotlib.image.AxesImage at 0x22a84cfecc0>

CelebA

The CelebFaces Attributes Dataset (CelebA) dataset contains over 200,000 celebrity images with annotations. Since you're going to be generating faces, you won't need the annotations. You can view the first number of examples by changing show_n_images.

In [28]:
show_n_images = 25

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'img_align_celeba/*.jpg'))[:show_n_images], 28, 28, 'RGB')
pyplot.imshow(helper.images_square_grid(mnist_images, 'RGB'))
Out[28]:
<matplotlib.image.AxesImage at 0x22a8d8d9400>

Preprocess the Data

Since the project's main focus is on building the GANs, we'll preprocess the data for you. The values of the MNIST and CelebA dataset will be in the range of -0.5 to 0.5 of 28x28 dimensional images. The CelebA images will be cropped to remove parts of the image that don't include a face, then resized down to 28x28.

The MNIST images are black and white images with a single color channel while the CelebA images have 3 color channels (RGB color channel).

Build the Neural Network

You'll build the components necessary to build a GANs by implementing the following functions below:

  • model_inputs
  • discriminator
  • generator
  • model_loss
  • model_opt
  • train

Check the Version of TensorFlow and Access to GPU

This will check to make sure you have the correct version of TensorFlow and access to a GPU

In [29]:
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
from distutils.version import LooseVersion
import warnings
import tensorflow as tf

# Check TensorFlow Version
assert LooseVersion(tf.__version__) >= LooseVersion('1.0'), 'Please use TensorFlow version 1.0 or newer.  You are using {}'.format(tf.__version__)
print('TensorFlow Version: {}'.format(tf.__version__))

# Check for a GPU
if not tf.test.gpu_device_name():
    warnings.warn('No GPU found. Please use a GPU to train your neural network.')
else:
    print('Default GPU Device: {}'.format(tf.test.gpu_device_name()))
TensorFlow Version: 1.1.0
f:\program files\python\python36\lib\site-packages\ipykernel\__main__.py:14: UserWarning: No GPU found. Please use a GPU to train your neural network.

Input

Implement the model_inputs function to create TF Placeholders for the Neural Network. It should create the following placeholders:

  • Real input images placeholder with rank 4 using image_width, image_height, and image_channels.
  • Z input placeholder with rank 2 using z_dim.
  • Learning rate placeholder with rank 0.

Return the placeholders in the following the tuple (tensor of real input images, tensor of z data)

In [30]:
import problem_unittests as tests

def model_inputs(image_width, image_height, image_channels, z_dim):
    """
    Create the model inputs
    :param image_width: The input image width
    :param image_height: The input image height
    :param image_channels: The number of image channels
    :param z_dim: The dimension of Z
    :return: Tuple of (tensor of real input images, tensor of z data, learning rate)
    """
    # TODO: Implement Function
    real_inputs = tf.placeholder(tf.float32,(None,image_width, image_height, image_channels),name='real_inputs')
    z_inputs = tf.placeholder(tf.float32,(None,z_dim),name='z_inputs')
    learning_rate = tf.placeholder(tf.float32,name='learning_rate')
    
    return real_inputs, z_inputs, learning_rate


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_inputs(model_inputs)
Tests Passed

Discriminator

Implement discriminator to create a discriminator neural network that discriminates on images. This function should be able to reuse the variabes in the neural network. Use tf.variable_scope with a scope name of "discriminator" to allow the variables to be reused. The function should return a tuple of (tensor output of the generator, tensor logits of the generator).

In [31]:
def discriminator(images, reuse=False):
    """
    Create the discriminator network
    :param image: Tensor of input image(s)
    :param reuse: Boolean if the weights should be reused
    :return: Tuple of (tensor output of the discriminator, tensor logits of the discriminator)
    """
    # TODO: Implement Function

    with tf.variable_scope("discriminator", reuse = reuse):
         
    
        x1 = tf.layers.conv2d(images, 64, 5, strides=2, padding='same',kernel_initializer =tf.random_normal_initializer(stddev=0.05))
        relu1 = tf.maximum(0.2 * x1, x1)
    
    
        x2 = tf.layers.conv2d(relu1, 128, 5, strides=2, padding='same',kernel_initializer =tf.random_normal_initializer(stddev=0.05))
        bn2 = tf.layers.batch_normalization(x2, training=True)
        relu2 = tf.maximum(0.2 * bn2, bn2)
       
        
        x3 = tf.layers.conv2d(relu2, 256, 5, strides=2, padding='same',kernel_initializer =tf.random_normal_initializer(stddev=0.05))
        bn3 = tf.layers.batch_normalization(x3, training=True)
        relu3 = tf.maximum(0.2 * bn3, bn3)
        
        
        
        flat = tf.reshape(relu3, (-1, 4*4*256))
        logits = tf.layers.dense(flat, 1)
        out = tf.sigmoid(logits)

    return out, logits 


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_discriminator(discriminator, tf)
Tests Passed

Generator

Implement generator to generate an image using z. This function should be able to reuse the variabes in the neural network. Use tf.variable_scope with a scope name of "generator" to allow the variables to be reused. The function should return the generated 28 x 28 x out_channel_dim images.

In [32]:
def generator(z, out_channel_dim, is_train=True):
    """
    Create the generator network
    :param z: Input z
    :param out_channel_dim: The number of channels in the output image
    :param is_train: Boolean if generator is being used for training
    :return: The tensor output of the generator
    """
    # TODO: Implement Function
    
    with tf.variable_scope('generator',reuse = not is_train):
        # First fully connected layer
        x1 = tf.layers.dense(z, 7*7*256)
        
        x1 = tf.reshape(x1, (-1, 7, 7, 256))
        x1 = tf.layers.batch_normalization(x1, training=is_train)
        x1 = tf.maximum(0.2 * x1, x1)
       
        
        x2 = tf.layers.conv2d_transpose(x1, 128, 5, strides=2, padding='same',kernel_initializer =tf.random_normal_initializer(stddev=0.05))
        x2 = tf.layers.batch_normalization(x2, training=is_train)
        x2 = tf.maximum(0.2 * x2, x2)
        
        
        x3 = tf.layers.conv2d_transpose(x2, 64, 5, strides=2, padding='same',kernel_initializer =tf.random_normal_initializer(stddev=0.05))
        x3 = tf.layers.batch_normalization(x3, training=is_train)
        x3 = tf.maximum(0.2 * x3, x3)
       
        
        logits = tf.layers.conv2d_transpose(x3, out_channel_dim, 3, strides=1, padding='same',kernel_initializer =tf.random_normal_initializer(stddev=0.05))
        
        
        out = tf.tanh(logits)
        
  
    return out


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_generator(generator, tf)
Tests Passed

Loss

Implement model_loss to build the GANs for training and calculate the loss. The function should return a tuple of (discriminator loss, generator loss). Use the following functions you implemented:

  • discriminator(images, reuse=False)
  • generator(z, out_channel_dim, is_train=True)
In [33]:
def model_loss(input_real, input_z, out_channel_dim):
    """
    Get the loss for the discriminator and generator
    :param input_real: Images from the real dataset
    :param input_z: Z input
    :param out_channel_dim: The number of channels in the output image
    :return: A tuple of (discriminator loss, generator loss)
    """
    # TODO: Implement Function
    smooth = 0.1
    g_model = generator(input_z, out_channel_dim)
    d_model_real, d_logits_real = discriminator(input_real)
    d_model_fake, d_logits_fake = discriminator(g_model, reuse=True)

    d_loss_real = tf.reduce_mean(
        tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_real, labels=tf.ones_like(d_model_real) * (1 - smooth)))
    d_loss_fake = tf.reduce_mean(
        tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake, labels=tf.zeros_like(d_model_fake)))
    g_loss = tf.reduce_mean(
        tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake, labels=tf.ones_like(d_model_fake)))

    d_loss = d_loss_real + d_loss_fake
    return d_loss, g_loss


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_loss(model_loss)
Tests Passed

Optimization

Implement model_opt to create the optimization operations for the GANs. Use tf.trainable_variables to get all the trainable variables. Filter the variables with names that are in the discriminator and generator scope names. The function should return a tuple of (discriminator training operation, generator training operation).

In [34]:
def model_opt(d_loss, g_loss, learning_rate, beta1):
    """
    Get optimization operations
    :param d_loss: Discriminator loss Tensor
    :param g_loss: Generator loss Tensor
    :param learning_rate: Learning Rate Placeholder
    :param beta1: The exponential decay rate for the 1st moment in the optimizer
    :return: A tuple of (discriminator training operation, generator training operation)
    """
    # TODO: Implement Function
    
    t_vars = tf.trainable_variables()
    g_vars = [var for var in t_vars if var.name.startswith('generator')]
    d_vars = [var for var in t_vars if var.name.startswith('discriminator')]

    d_train_opt = tf.train.AdamOptimizer(learning_rate=learning_rate, beta1=beta1).minimize(d_loss, var_list=d_vars)
    g_train_opt = tf.train.AdamOptimizer(learning_rate=learning_rate,beta1=beta1).minimize(g_loss, var_list=g_vars)
    
    return d_train_opt, g_train_opt


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_opt(model_opt, tf)
Tests Passed

Neural Network Training

Show Output

Use this function to show the current output of the generator during training. It will help you determine how well the GANs is training.

In [35]:
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import numpy as np

def show_generator_output(sess, n_images, input_z, out_channel_dim, image_mode):
    """
    Show example output for the generator
    :param sess: TensorFlow session
    :param n_images: Number of Images to display
    :param input_z: Input Z Tensor
    :param out_channel_dim: The number of channels in the output image
    :param image_mode: The mode to use for images ("RGB" or "L")
    """
    cmap = None if image_mode == 'RGB' else 'gray'
    z_dim = input_z.get_shape().as_list()[-1]
    example_z = np.random.uniform(-1, 1, size=[n_images, z_dim])

    samples = sess.run(
        generator(input_z, out_channel_dim, False),
        feed_dict={input_z: example_z})

    images_grid = helper.images_square_grid(samples, image_mode)
    pyplot.imshow(images_grid, cmap=cmap)
    pyplot.show()

Train

Implement train to build and train the GANs. Use the following functions you implemented:

  • model_inputs(image_width, image_height, image_channels, z_dim)
  • model_loss(input_real, input_z, out_channel_dim)
  • model_opt(d_loss, g_loss, learning_rate, beta1)

Use the show_generator_output to show generator output while you train. Running show_generator_output for every batch will drastically increase training time and increase the size of the notebook. It's recommended to print the generator output every 100 batches.

In [36]:
def train(epoch_count, batch_size, z_dim, learning_rate, beta1, get_batches, data_shape, data_image_mode):
    """
    Train the GAN
    :param epoch_count: Number of epochs
    :param batch_size: Batch Size
    :param z_dim: Z dimension
    :param learning_rate: Learning Rate
    :param beta1: The exponential decay rate for the 1st moment in the optimizer
    :param get_batches: Function to get batches
    :param data_shape: Shape of the data
    :param data_image_mode: The image mode to use for images ("RGB" or "L")
    """
    # TODO: Build Model
    steps = 0
    _, image_width, image_height, image_channels = data_shape
    input_real, input_z, lr = model_inputs(image_width, image_height,image_channels, z_dim)
    d_loss, g_loss = model_loss(input_real, input_z,image_channels)
    d_opt, g_opt = model_opt(d_loss, g_loss, lr, beta1)
        
    with tf.Session() as sess:
        sess.run(tf.global_variables_initializer())
        for epoch_i in range(epoch_count):
            for batch_images in get_batches(batch_size):
                # TODO: Train Model
                steps += 1
                batch_images = batch_images * 2
                batch_z = np.random.uniform(-1, 1, size=(batch_size, z_dim))
                _ = sess.run(d_opt, feed_dict={input_real: batch_images, input_z: batch_z, lr:learning_rate})
                _ = sess.run(g_opt, feed_dict={input_z: batch_z, lr:learning_rate})
                
                if steps % 10 == 0:
                    train_loss_d = d_loss.eval({input_z: batch_z, input_real: batch_images})
                    train_loss_g = g_loss.eval({input_z: batch_z})

                    print("Epoch {}/{}...".format(epoch_i+1, epoch_count),
                          "Discriminator Loss: {:.4f}...".format(train_loss_d),
                          "Generator Loss: {:.4f}".format(train_loss_g))

                if steps % 100 == 0:
                    show_generator_output(sess, 50, input_z, image_channels, data_image_mode)

MNIST

Test your GANs architecture on MNIST. After 2 epochs, the GANs should be able to generate images that look like handwritten digits. Make sure the loss of the generator is lower than the loss of the discriminator or close to 0.

In [40]:
batch_size = 64
z_dim = 100
learning_rate = 0.0001
beta1 = 0.5


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 2

mnist_dataset = helper.Dataset('mnist', glob(os.path.join(data_dir, 'mnist/*.jpg')))
with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, mnist_dataset.get_batches,
          mnist_dataset.shape, mnist_dataset.image_mode)
Epoch 1/2... Discriminator Loss: 1.3413... Generator Loss: 0.5937
Epoch 1/2... Discriminator Loss: 1.8909... Generator Loss: 0.9998
Epoch 1/2... Discriminator Loss: 1.6587... Generator Loss: 1.2160
Epoch 1/2... Discriminator Loss: 1.3253... Generator Loss: 1.4366
Epoch 1/2... Discriminator Loss: 1.5401... Generator Loss: 0.6091
Epoch 1/2... Discriminator Loss: 1.2203... Generator Loss: 0.8528
Epoch 1/2... Discriminator Loss: 1.4568... Generator Loss: 0.5893
Epoch 1/2... Discriminator Loss: 1.4414... Generator Loss: 1.1924
Epoch 1/2... Discriminator Loss: 1.2021... Generator Loss: 1.0527
Epoch 1/2... Discriminator Loss: 1.4403... Generator Loss: 0.5629
Epoch 1/2... Discriminator Loss: 1.4524... Generator Loss: 0.4900
Epoch 1/2... Discriminator Loss: 1.3046... Generator Loss: 0.7981
Epoch 1/2... Discriminator Loss: 1.2491... Generator Loss: 0.6960
Epoch 1/2... Discriminator Loss: 1.6582... Generator Loss: 1.9086
Epoch 1/2... Discriminator Loss: 1.2508... Generator Loss: 0.9100
Epoch 1/2... Discriminator Loss: 1.1953... Generator Loss: 0.8320
Epoch 1/2... Discriminator Loss: 1.5256... Generator Loss: 2.3088
Epoch 1/2... Discriminator Loss: 1.0963... Generator Loss: 0.9213
Epoch 1/2... Discriminator Loss: 1.3893... Generator Loss: 0.5339
Epoch 1/2... Discriminator Loss: 0.9408... Generator Loss: 1.7912
Epoch 1/2... Discriminator Loss: 1.0363... Generator Loss: 1.5098
Epoch 1/2... Discriminator Loss: 1.1765... Generator Loss: 0.7481
Epoch 1/2... Discriminator Loss: 1.0504... Generator Loss: 0.9659
Epoch 1/2... Discriminator Loss: 1.2848... Generator Loss: 0.6371
Epoch 1/2... Discriminator Loss: 0.9464... Generator Loss: 0.9719
Epoch 1/2... Discriminator Loss: 1.0327... Generator Loss: 1.8111
Epoch 1/2... Discriminator Loss: 0.9022... Generator Loss: 1.1445
Epoch 1/2... Discriminator Loss: 1.3873... Generator Loss: 0.5103
Epoch 1/2... Discriminator Loss: 1.0172... Generator Loss: 1.1681
Epoch 1/2... Discriminator Loss: 0.8436... Generator Loss: 1.1205
Epoch 1/2... Discriminator Loss: 0.9156... Generator Loss: 1.4114
Epoch 1/2... Discriminator Loss: 0.9209... Generator Loss: 1.3605
Epoch 1/2... Discriminator Loss: 0.8763... Generator Loss: 1.3719
Epoch 1/2... Discriminator Loss: 0.8655... Generator Loss: 1.1986
Epoch 1/2... Discriminator Loss: 0.8978... Generator Loss: 1.3549
Epoch 1/2... Discriminator Loss: 1.3661... Generator Loss: 0.5579
Epoch 1/2... Discriminator Loss: 0.9102... Generator Loss: 1.0243
Epoch 1/2... Discriminator Loss: 1.0504... Generator Loss: 0.8268
Epoch 1/2... Discriminator Loss: 1.0831... Generator Loss: 0.8985
Epoch 1/2... Discriminator Loss: 1.0683... Generator Loss: 0.9575
Epoch 1/2... Discriminator Loss: 1.0766... Generator Loss: 0.8314
Epoch 1/2... Discriminator Loss: 0.8286... Generator Loss: 1.4117
Epoch 1/2... Discriminator Loss: 1.1613... Generator Loss: 1.8568
Epoch 1/2... Discriminator Loss: 1.0427... Generator Loss: 1.0910
Epoch 1/2... Discriminator Loss: 0.9015... Generator Loss: 1.5687
Epoch 1/2... Discriminator Loss: 1.1069... Generator Loss: 0.8073
Epoch 1/2... Discriminator Loss: 1.2928... Generator Loss: 0.5453
Epoch 1/2... Discriminator Loss: 0.9441... Generator Loss: 1.0805
Epoch 1/2... Discriminator Loss: 1.0157... Generator Loss: 1.2013
Epoch 1/2... Discriminator Loss: 1.1168... Generator Loss: 1.1588
Epoch 1/2... Discriminator Loss: 1.2030... Generator Loss: 1.2291
Epoch 1/2... Discriminator Loss: 1.3923... Generator Loss: 0.5062
Epoch 1/2... Discriminator Loss: 0.9458... Generator Loss: 1.1968
Epoch 1/2... Discriminator Loss: 1.0298... Generator Loss: 0.9425
Epoch 1/2... Discriminator Loss: 1.1234... Generator Loss: 0.9477
Epoch 1/2... Discriminator Loss: 1.1839... Generator Loss: 0.8045
Epoch 1/2... Discriminator Loss: 1.3349... Generator Loss: 0.6179
Epoch 1/2... Discriminator Loss: 0.9643... Generator Loss: 1.0576
Epoch 1/2... Discriminator Loss: 1.1576... Generator Loss: 0.7596
Epoch 1/2... Discriminator Loss: 1.0396... Generator Loss: 1.2755
Epoch 1/2... Discriminator Loss: 1.0022... Generator Loss: 1.1495
Epoch 1/2... Discriminator Loss: 1.0890... Generator Loss: 1.8650
Epoch 1/2... Discriminator Loss: 1.1658... Generator Loss: 1.2515
Epoch 1/2... Discriminator Loss: 0.9613... Generator Loss: 1.0051
Epoch 1/2... Discriminator Loss: 1.2068... Generator Loss: 1.1329
Epoch 1/2... Discriminator Loss: 1.0608... Generator Loss: 1.0118
Epoch 1/2... Discriminator Loss: 1.0710... Generator Loss: 1.4745
Epoch 1/2... Discriminator Loss: 1.4066... Generator Loss: 0.5872
Epoch 1/2... Discriminator Loss: 0.9753... Generator Loss: 1.1676
Epoch 1/2... Discriminator Loss: 1.0797... Generator Loss: 1.2569
Epoch 1/2... Discriminator Loss: 1.3152... Generator Loss: 0.6193
Epoch 1/2... Discriminator Loss: 1.0071... Generator Loss: 0.9375
Epoch 1/2... Discriminator Loss: 1.1250... Generator Loss: 1.5411
Epoch 1/2... Discriminator Loss: 0.9632... Generator Loss: 1.1372
Epoch 1/2... Discriminator Loss: 0.9565... Generator Loss: 1.2260
Epoch 1/2... Discriminator Loss: 0.9945... Generator Loss: 0.9777
Epoch 1/2... Discriminator Loss: 1.1731... Generator Loss: 0.7069
Epoch 1/2... Discriminator Loss: 1.2798... Generator Loss: 0.6296
Epoch 1/2... Discriminator Loss: 1.2154... Generator Loss: 0.6743
Epoch 1/2... Discriminator Loss: 1.1742... Generator Loss: 0.6756
Epoch 1/2... Discriminator Loss: 1.0072... Generator Loss: 1.2925
Epoch 1/2... Discriminator Loss: 1.0185... Generator Loss: 1.3310
Epoch 1/2... Discriminator Loss: 1.0876... Generator Loss: 0.7995
Epoch 1/2... Discriminator Loss: 0.8488... Generator Loss: 1.6296
Epoch 1/2... Discriminator Loss: 0.8594... Generator Loss: 1.5378
Epoch 1/2... Discriminator Loss: 2.7511... Generator Loss: 0.1768
Epoch 1/2... Discriminator Loss: 1.2413... Generator Loss: 1.3012
Epoch 1/2... Discriminator Loss: 0.9649... Generator Loss: 1.0705
Epoch 1/2... Discriminator Loss: 0.9881... Generator Loss: 1.2318
Epoch 1/2... Discriminator Loss: 0.9854... Generator Loss: 1.1438
Epoch 1/2... Discriminator Loss: 1.0142... Generator Loss: 0.9835
Epoch 1/2... Discriminator Loss: 0.9105... Generator Loss: 1.3687
Epoch 1/2... Discriminator Loss: 1.1171... Generator Loss: 0.7851
Epoch 2/2... Discriminator Loss: 1.0203... Generator Loss: 0.8637
Epoch 2/2... Discriminator Loss: 0.9113... Generator Loss: 1.1093
Epoch 2/2... Discriminator Loss: 0.9655... Generator Loss: 1.0692
Epoch 2/2... Discriminator Loss: 0.9542... Generator Loss: 1.2168
Epoch 2/2... Discriminator Loss: 1.0658... Generator Loss: 0.9059
Epoch 2/2... Discriminator Loss: 0.9628... Generator Loss: 1.4367
Epoch 2/2... Discriminator Loss: 2.0295... Generator Loss: 2.6591
Epoch 2/2... Discriminator Loss: 0.9355... Generator Loss: 1.2419
Epoch 2/2... Discriminator Loss: 0.9589... Generator Loss: 1.4265
Epoch 2/2... Discriminator Loss: 0.8851... Generator Loss: 1.1229
Epoch 2/2... Discriminator Loss: 0.9876... Generator Loss: 1.0589
Epoch 2/2... Discriminator Loss: 0.8741... Generator Loss: 1.1353
Epoch 2/2... Discriminator Loss: 1.1262... Generator Loss: 0.7338
Epoch 2/2... Discriminator Loss: 1.0553... Generator Loss: 1.1876
Epoch 2/2... Discriminator Loss: 1.3090... Generator Loss: 0.5716
Epoch 2/2... Discriminator Loss: 0.9550... Generator Loss: 1.0478
Epoch 2/2... Discriminator Loss: 1.5295... Generator Loss: 0.4450
Epoch 2/2... Discriminator Loss: 1.5585... Generator Loss: 0.4931
Epoch 2/2... Discriminator Loss: 0.7935... Generator Loss: 1.4370
Epoch 2/2... Discriminator Loss: 0.8843... Generator Loss: 1.2858
Epoch 2/2... Discriminator Loss: 0.8857... Generator Loss: 1.2168
Epoch 2/2... Discriminator Loss: 0.9851... Generator Loss: 1.0425
Epoch 2/2... Discriminator Loss: 1.2419... Generator Loss: 0.6163
Epoch 2/2... Discriminator Loss: 1.0541... Generator Loss: 1.3379
Epoch 2/2... Discriminator Loss: 1.1873... Generator Loss: 0.6898
Epoch 2/2... Discriminator Loss: 1.0470... Generator Loss: 0.8719
Epoch 2/2... Discriminator Loss: 0.9082... Generator Loss: 1.1619
Epoch 2/2... Discriminator Loss: 0.7689... Generator Loss: 1.5866
Epoch 2/2... Discriminator Loss: 1.7512... Generator Loss: 2.8828
Epoch 2/2... Discriminator Loss: 0.9816... Generator Loss: 1.0513
Epoch 2/2... Discriminator Loss: 1.0625... Generator Loss: 0.8991
Epoch 2/2... Discriminator Loss: 0.9508... Generator Loss: 1.4144
Epoch 2/2... Discriminator Loss: 0.9240... Generator Loss: 1.5083
Epoch 2/2... Discriminator Loss: 1.3818... Generator Loss: 0.5003
Epoch 2/2... Discriminator Loss: 1.0752... Generator Loss: 0.8383
Epoch 2/2... Discriminator Loss: 0.8873... Generator Loss: 1.3189
Epoch 2/2... Discriminator Loss: 1.3267... Generator Loss: 1.8098
Epoch 2/2... Discriminator Loss: 0.9775... Generator Loss: 1.0374
Epoch 2/2... Discriminator Loss: 1.1415... Generator Loss: 1.0670
Epoch 2/2... Discriminator Loss: 0.9127... Generator Loss: 1.0524
Epoch 2/2... Discriminator Loss: 1.1012... Generator Loss: 0.7752
Epoch 2/2... Discriminator Loss: 1.0214... Generator Loss: 1.6654
Epoch 2/2... Discriminator Loss: 0.8668... Generator Loss: 1.8291
Epoch 2/2... Discriminator Loss: 0.8497... Generator Loss: 1.4963
Epoch 2/2... Discriminator Loss: 0.9045... Generator Loss: 1.4050
Epoch 2/2... Discriminator Loss: 0.9027... Generator Loss: 1.2876
Epoch 2/2... Discriminator Loss: 1.2139... Generator Loss: 1.8647
Epoch 2/2... Discriminator Loss: 0.9903... Generator Loss: 1.0064
Epoch 2/2... Discriminator Loss: 0.9040... Generator Loss: 1.5359
Epoch 2/2... Discriminator Loss: 1.2558... Generator Loss: 0.6315
Epoch 2/2... Discriminator Loss: 0.8190... Generator Loss: 1.5289
Epoch 2/2... Discriminator Loss: 0.9354... Generator Loss: 1.4343
Epoch 2/2... Discriminator Loss: 0.8951... Generator Loss: 1.1724
Epoch 2/2... Discriminator Loss: 1.0157... Generator Loss: 0.8933
Epoch 2/2... Discriminator Loss: 0.8666... Generator Loss: 1.1095
Epoch 2/2... Discriminator Loss: 1.1221... Generator Loss: 0.7223
Epoch 2/2... Discriminator Loss: 0.7629... Generator Loss: 1.7664
Epoch 2/2... Discriminator Loss: 0.9714... Generator Loss: 1.0940
Epoch 2/2... Discriminator Loss: 0.9478... Generator Loss: 1.0201
Epoch 2/2... Discriminator Loss: 0.9177... Generator Loss: 1.5633
Epoch 2/2... Discriminator Loss: 0.8487... Generator Loss: 1.1524
Epoch 2/2... Discriminator Loss: 0.8149... Generator Loss: 1.4401
Epoch 2/2... Discriminator Loss: 0.9952... Generator Loss: 0.9619
Epoch 2/2... Discriminator Loss: 0.9763... Generator Loss: 1.0698
Epoch 2/2... Discriminator Loss: 0.9368... Generator Loss: 1.4373
Epoch 2/2... Discriminator Loss: 1.3299... Generator Loss: 0.5640
Epoch 2/2... Discriminator Loss: 1.5138... Generator Loss: 3.1478
Epoch 2/2... Discriminator Loss: 1.0644... Generator Loss: 0.8306
Epoch 2/2... Discriminator Loss: 0.8709... Generator Loss: 1.3147
Epoch 2/2... Discriminator Loss: 1.0210... Generator Loss: 0.9721
Epoch 2/2... Discriminator Loss: 1.0116... Generator Loss: 1.1478
Epoch 2/2... Discriminator Loss: 0.9029... Generator Loss: 2.2311
Epoch 2/2... Discriminator Loss: 0.8513... Generator Loss: 1.3036
Epoch 2/2... Discriminator Loss: 0.8904... Generator Loss: 1.1489
Epoch 2/2... Discriminator Loss: 0.7956... Generator Loss: 1.9019
Epoch 2/2... Discriminator Loss: 1.0045... Generator Loss: 0.9458
Epoch 2/2... Discriminator Loss: 1.1980... Generator Loss: 0.6680
Epoch 2/2... Discriminator Loss: 1.0660... Generator Loss: 0.9450
Epoch 2/2... Discriminator Loss: 1.0860... Generator Loss: 0.7916
Epoch 2/2... Discriminator Loss: 1.2751... Generator Loss: 0.5724
Epoch 2/2... Discriminator Loss: 1.0791... Generator Loss: 0.9562
Epoch 2/2... Discriminator Loss: 0.8371... Generator Loss: 1.2272
Epoch 2/2... Discriminator Loss: 0.8263... Generator Loss: 1.4773
Epoch 2/2... Discriminator Loss: 0.9553... Generator Loss: 1.0089
Epoch 2/2... Discriminator Loss: 1.9578... Generator Loss: 0.3493
Epoch 2/2... Discriminator Loss: 0.6700... Generator Loss: 2.0187
Epoch 2/2... Discriminator Loss: 0.9597... Generator Loss: 1.0112
Epoch 2/2... Discriminator Loss: 0.9939... Generator Loss: 1.1974
Epoch 2/2... Discriminator Loss: 1.0420... Generator Loss: 1.0281
Epoch 2/2... Discriminator Loss: 0.7719... Generator Loss: 1.4734
Epoch 2/2... Discriminator Loss: 0.9893... Generator Loss: 1.0022
Epoch 2/2... Discriminator Loss: 0.8410... Generator Loss: 1.7490
Epoch 2/2... Discriminator Loss: 0.8265... Generator Loss: 1.7377
Epoch 2/2... Discriminator Loss: 0.8436... Generator Loss: 1.3815

CelebA

Run your GANs on CelebA. It will take around 20 minutes on the average GPU to run one epoch. You can run the whole epoch or stop when it starts to generate realistic faces.

In [42]:
batch_size = 128
z_dim = 100
learning_rate = 0.0005
beta1 = 0.6


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 1

celeba_dataset = helper.Dataset('celeba', glob(os.path.join(data_dir, 'img_align_celeba/*.jpg')))
with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, celeba_dataset.get_batches,
          celeba_dataset.shape, celeba_dataset.image_mode)
Epoch 1/1... Discriminator Loss: 1.0071... Generator Loss: 1.0660
Epoch 1/1... Discriminator Loss: 1.2414... Generator Loss: 0.7265
Epoch 1/1... Discriminator Loss: 0.5347... Generator Loss: 2.4771
Epoch 1/1... Discriminator Loss: 0.6815... Generator Loss: 2.7093
Epoch 1/1... Discriminator Loss: 1.1534... Generator Loss: 0.8148
Epoch 1/1... Discriminator Loss: 1.3719... Generator Loss: 0.5758
Epoch 1/1... Discriminator Loss: 0.8753... Generator Loss: 2.9928
Epoch 1/1... Discriminator Loss: 0.9520... Generator Loss: 2.5648
Epoch 1/1... Discriminator Loss: 0.8598... Generator Loss: 1.6971
Epoch 1/1... Discriminator Loss: 1.0151... Generator Loss: 2.2809
Epoch 1/1... Discriminator Loss: 1.1682... Generator Loss: 2.1466
Epoch 1/1... Discriminator Loss: 0.7298... Generator Loss: 1.6966
Epoch 1/1... Discriminator Loss: 1.5308... Generator Loss: 1.4392
Epoch 1/1... Discriminator Loss: 0.8843... Generator Loss: 1.4881
Epoch 1/1... Discriminator Loss: 1.1468... Generator Loss: 1.0364
Epoch 1/1... Discriminator Loss: 0.8723... Generator Loss: 1.9607
Epoch 1/1... Discriminator Loss: 0.9977... Generator Loss: 1.2353
Epoch 1/1... Discriminator Loss: 1.0481... Generator Loss: 1.1845
Epoch 1/1... Discriminator Loss: 1.0273... Generator Loss: 3.1886
Epoch 1/1... Discriminator Loss: 1.2873... Generator Loss: 1.0892
Epoch 1/1... Discriminator Loss: 1.2143... Generator Loss: 0.9283
Epoch 1/1... Discriminator Loss: 1.2687... Generator Loss: 0.9233
Epoch 1/1... Discriminator Loss: 1.3213... Generator Loss: 1.0914
Epoch 1/1... Discriminator Loss: 1.2992... Generator Loss: 0.9445
Epoch 1/1... Discriminator Loss: 1.2978... Generator Loss: 0.7548
Epoch 1/1... Discriminator Loss: 1.3680... Generator Loss: 0.7150
Epoch 1/1... Discriminator Loss: 1.2753... Generator Loss: 1.2764
Epoch 1/1... Discriminator Loss: 1.7061... Generator Loss: 0.9115
Epoch 1/1... Discriminator Loss: 1.9007... Generator Loss: 0.3421
Epoch 1/1... Discriminator Loss: 1.2939... Generator Loss: 1.1546
Epoch 1/1... Discriminator Loss: 1.2268... Generator Loss: 1.0278
Epoch 1/1... Discriminator Loss: 1.4231... Generator Loss: 0.9908
Epoch 1/1... Discriminator Loss: 1.0633... Generator Loss: 0.9905
Epoch 1/1... Discriminator Loss: 1.3240... Generator Loss: 0.7480
Epoch 1/1... Discriminator Loss: 1.2957... Generator Loss: 1.0061
Epoch 1/1... Discriminator Loss: 1.2719... Generator Loss: 0.8364
Epoch 1/1... Discriminator Loss: 1.4140... Generator Loss: 0.8725
Epoch 1/1... Discriminator Loss: 1.4855... Generator Loss: 0.9773
Epoch 1/1... Discriminator Loss: 1.2474... Generator Loss: 0.8931
Epoch 1/1... Discriminator Loss: 1.6207... Generator Loss: 0.6346
Epoch 1/1... Discriminator Loss: 1.4381... Generator Loss: 0.9707
Epoch 1/1... Discriminator Loss: 1.2896... Generator Loss: 1.1649
Epoch 1/1... Discriminator Loss: 1.2127... Generator Loss: 0.9439
Epoch 1/1... Discriminator Loss: 1.3591... Generator Loss: 0.9673
Epoch 1/1... Discriminator Loss: 1.5317... Generator Loss: 0.6353
Epoch 1/1... Discriminator Loss: 1.3266... Generator Loss: 0.9890
Epoch 1/1... Discriminator Loss: 1.4140... Generator Loss: 0.9762
Epoch 1/1... Discriminator Loss: 1.4232... Generator Loss: 0.8097
Epoch 1/1... Discriminator Loss: 1.3230... Generator Loss: 1.1598
Epoch 1/1... Discriminator Loss: 1.5377... Generator Loss: 0.8706
Epoch 1/1... Discriminator Loss: 1.1392... Generator Loss: 1.3262
Epoch 1/1... Discriminator Loss: 1.1587... Generator Loss: 1.1229
Epoch 1/1... Discriminator Loss: 1.2391... Generator Loss: 0.9296
Epoch 1/1... Discriminator Loss: 1.4954... Generator Loss: 0.8623
Epoch 1/1... Discriminator Loss: 1.2659... Generator Loss: 0.8500
Epoch 1/1... Discriminator Loss: 1.3061... Generator Loss: 0.8401
Epoch 1/1... Discriminator Loss: 1.5092... Generator Loss: 0.7906
Epoch 1/1... Discriminator Loss: 1.3619... Generator Loss: 1.0401
Epoch 1/1... Discriminator Loss: 1.3025... Generator Loss: 1.1405
Epoch 1/1... Discriminator Loss: 1.3539... Generator Loss: 0.7809
Epoch 1/1... Discriminator Loss: 1.3389... Generator Loss: 1.0342
Epoch 1/1... Discriminator Loss: 1.3729... Generator Loss: 0.8719
Epoch 1/1... Discriminator Loss: 1.3346... Generator Loss: 0.7791
Epoch 1/1... Discriminator Loss: 1.2703... Generator Loss: 0.7821
Epoch 1/1... Discriminator Loss: 1.3223... Generator Loss: 0.8639
Epoch 1/1... Discriminator Loss: 1.3865... Generator Loss: 1.3766
Epoch 1/1... Discriminator Loss: 1.4152... Generator Loss: 0.6389
Epoch 1/1... Discriminator Loss: 1.2443... Generator Loss: 0.9239
Epoch 1/1... Discriminator Loss: 1.3054... Generator Loss: 0.7370
Epoch 1/1... Discriminator Loss: 1.3601... Generator Loss: 0.8775
Epoch 1/1... Discriminator Loss: 1.3693... Generator Loss: 0.8986
Epoch 1/1... Discriminator Loss: 1.2350... Generator Loss: 0.9811
Epoch 1/1... Discriminator Loss: 1.0983... Generator Loss: 0.8286
Epoch 1/1... Discriminator Loss: 1.3916... Generator Loss: 0.9924
Epoch 1/1... Discriminator Loss: 1.2700... Generator Loss: 0.7351
Epoch 1/1... Discriminator Loss: 1.1195... Generator Loss: 1.1394
Epoch 1/1... Discriminator Loss: 1.5619... Generator Loss: 0.7204
Epoch 1/1... Discriminator Loss: 1.1840... Generator Loss: 0.8494
Epoch 1/1... Discriminator Loss: 1.2887... Generator Loss: 1.0718
Epoch 1/1... Discriminator Loss: 1.4127... Generator Loss: 0.7657
Epoch 1/1... Discriminator Loss: 1.3388... Generator Loss: 0.7660
Epoch 1/1... Discriminator Loss: 1.3615... Generator Loss: 0.6522
Epoch 1/1... Discriminator Loss: 1.1721... Generator Loss: 0.9211
Epoch 1/1... Discriminator Loss: 1.2495... Generator Loss: 1.1132
Epoch 1/1... Discriminator Loss: 1.1652... Generator Loss: 1.1843
Epoch 1/1... Discriminator Loss: 1.4496... Generator Loss: 0.8743
Epoch 1/1... Discriminator Loss: 1.2889... Generator Loss: 0.8361
Epoch 1/1... Discriminator Loss: 1.3543... Generator Loss: 1.0273
Epoch 1/1... Discriminator Loss: 1.4229... Generator Loss: 1.1026
Epoch 1/1... Discriminator Loss: 1.2015... Generator Loss: 0.9371
Epoch 1/1... Discriminator Loss: 1.3265... Generator Loss: 0.8419
Epoch 1/1... Discriminator Loss: 1.1508... Generator Loss: 1.1590
Epoch 1/1... Discriminator Loss: 1.3012... Generator Loss: 0.8808
Epoch 1/1... Discriminator Loss: 1.4225... Generator Loss: 0.6121
Epoch 1/1... Discriminator Loss: 1.2564... Generator Loss: 0.8937
Epoch 1/1... Discriminator Loss: 1.3984... Generator Loss: 0.9024
Epoch 1/1... Discriminator Loss: 1.2167... Generator Loss: 0.9401
Epoch 1/1... Discriminator Loss: 1.2636... Generator Loss: 0.8184
Epoch 1/1... Discriminator Loss: 1.3040... Generator Loss: 0.9195
Epoch 1/1... Discriminator Loss: 1.4524... Generator Loss: 0.5620
Epoch 1/1... Discriminator Loss: 1.2534... Generator Loss: 0.8687
Epoch 1/1... Discriminator Loss: 1.3383... Generator Loss: 0.6678
Epoch 1/1... Discriminator Loss: 1.1946... Generator Loss: 0.9897
Epoch 1/1... Discriminator Loss: 1.2529... Generator Loss: 0.9470
Epoch 1/1... Discriminator Loss: 1.4223... Generator Loss: 0.8621
Epoch 1/1... Discriminator Loss: 1.3576... Generator Loss: 1.0713
Epoch 1/1... Discriminator Loss: 1.2874... Generator Loss: 0.9156
Epoch 1/1... Discriminator Loss: 1.3072... Generator Loss: 0.8660
Epoch 1/1... Discriminator Loss: 1.2979... Generator Loss: 0.8218
Epoch 1/1... Discriminator Loss: 1.2461... Generator Loss: 0.9370
Epoch 1/1... Discriminator Loss: 1.3636... Generator Loss: 0.9757
Epoch 1/1... Discriminator Loss: 1.2064... Generator Loss: 0.8366
Epoch 1/1... Discriminator Loss: 1.3067... Generator Loss: 0.8212
Epoch 1/1... Discriminator Loss: 1.2118... Generator Loss: 0.9847
Epoch 1/1... Discriminator Loss: 1.1778... Generator Loss: 1.0660
Epoch 1/1... Discriminator Loss: 1.2666... Generator Loss: 1.1189
Epoch 1/1... Discriminator Loss: 1.1744... Generator Loss: 0.9193
Epoch 1/1... Discriminator Loss: 1.1750... Generator Loss: 1.0646
Epoch 1/1... Discriminator Loss: 1.2664... Generator Loss: 1.2359
Epoch 1/1... Discriminator Loss: 1.4451... Generator Loss: 0.9131
Epoch 1/1... Discriminator Loss: 1.2240... Generator Loss: 0.8329
Epoch 1/1... Discriminator Loss: 1.1530... Generator Loss: 1.2009
Epoch 1/1... Discriminator Loss: 1.1991... Generator Loss: 1.0479
Epoch 1/1... Discriminator Loss: 1.3594... Generator Loss: 0.7668
Epoch 1/1... Discriminator Loss: 1.2183... Generator Loss: 0.8860
Epoch 1/1... Discriminator Loss: 1.1734... Generator Loss: 1.4263
Epoch 1/1... Discriminator Loss: 1.2246... Generator Loss: 0.8506
Epoch 1/1... Discriminator Loss: 1.1175... Generator Loss: 1.3381
Epoch 1/1... Discriminator Loss: 1.1729... Generator Loss: 2.0592
Epoch 1/1... Discriminator Loss: 1.1599... Generator Loss: 2.0743
Epoch 1/1... Discriminator Loss: 1.2551... Generator Loss: 0.9967
Epoch 1/1... Discriminator Loss: 1.2112... Generator Loss: 1.0395
Epoch 1/1... Discriminator Loss: 1.2002... Generator Loss: 2.1689
Epoch 1/1... Discriminator Loss: 1.4082... Generator Loss: 0.5579
Epoch 1/1... Discriminator Loss: 1.1667... Generator Loss: 1.2614
Epoch 1/1... Discriminator Loss: 1.4467... Generator Loss: 0.6343
Epoch 1/1... Discriminator Loss: 1.1381... Generator Loss: 0.9520
Epoch 1/1... Discriminator Loss: 1.3279... Generator Loss: 0.6578
Epoch 1/1... Discriminator Loss: 1.4247... Generator Loss: 0.7515
Epoch 1/1... Discriminator Loss: 1.2932... Generator Loss: 0.7457
Epoch 1/1... Discriminator Loss: 1.3852... Generator Loss: 0.6420
Epoch 1/1... Discriminator Loss: 0.8162... Generator Loss: 1.9345
Epoch 1/1... Discriminator Loss: 0.8064... Generator Loss: 2.0984
Epoch 1/1... Discriminator Loss: 1.0478... Generator Loss: 0.9826
Epoch 1/1... Discriminator Loss: 1.3077... Generator Loss: 0.7160
Epoch 1/1... Discriminator Loss: 1.3096... Generator Loss: 0.9382
Epoch 1/1... Discriminator Loss: 1.2714... Generator Loss: 0.9565
Epoch 1/1... Discriminator Loss: 1.1657... Generator Loss: 1.0216
Epoch 1/1... Discriminator Loss: 1.2077... Generator Loss: 1.0531
Epoch 1/1... Discriminator Loss: 1.3668... Generator Loss: 0.5311
Epoch 1/1... Discriminator Loss: 1.1254... Generator Loss: 1.0755
Epoch 1/1... Discriminator Loss: 1.1774... Generator Loss: 1.3596
Epoch 1/1... Discriminator Loss: 1.2555... Generator Loss: 0.8341
Epoch 1/1... Discriminator Loss: 1.2600... Generator Loss: 1.1694
Epoch 1/1... Discriminator Loss: 1.3335... Generator Loss: 1.0358
Epoch 1/1... Discriminator Loss: 1.2738... Generator Loss: 0.7794
Epoch 1/1... Discriminator Loss: 1.3312... Generator Loss: 1.2373
Epoch 1/1... Discriminator Loss: 1.3338... Generator Loss: 1.1339

Submitting This Project

When submitting this project, make sure to run all the cells before saving the notebook. Save the notebook file as "dlnd_face_generation.ipynb" and save it as a HTML file under "File" -> "Download as". Include the "helper.py" and "problem_unittests.py" files in your submission.